Day 0: Introduction and Project Setup

Overview

Welcome to Day 1 of your AI Quiz Platform journey! Today you'll establish the foundation for your entire project by learning proper requirements analysis and setting up a robust development environment. This lesson teaches you how software engineers approach new projects systematically, ensuring success from the very beginning.

Think of today as laying the cornerstone of a building - everything else will depend on getting this foundation right. You'll learn to think like a product manager when gathering requirements and like a DevOps engineer when setting up your development workflow.

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1. PREPARATION (15 minutes)

Previous Knowledge Required

Before starting this lesson, you should understand basic programming concepts and have familiarity with command-line interfaces. You don't need prior experience with Node.js or professional software development - we'll build that knowledge together.

Development Environment Setup/Verification

Let's verify your system is ready for professional development:

Check your operating system capabilities:

• Windows 10/11, macOS 10.15+, or Linux Ubuntu 18.04+

• At least 8GB RAM and 20GB free disk space

• Administrative privileges to install software

Internet connectivity requirements:

• Stable internet connection for API calls and package downloads

• Access to GitHub, npm registry, and Claude API

Tools and Dependencies Needed for Today

We'll install these tools step-by-step during implementation:

1. Node.js v18+ - JavaScript runtime for backend development

2. Git - Version control system for tracking code changes

3. Visual Studio Code - Professional code editor with extensions

4. GitHub Account - Cloud repository hosting

5. Claude API Access - AI service for question generation

Configuration Needed Before Starting

Ensure you have:

• A personal email address for GitHub account creation

• Permission to install software on your computer

• A quiet workspace for focused development time

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2. USER STORY AND REQUIREMENTS (30 minutes)

Primary User Story

As a student interested in learning various subjects, I want an AI-powered quiz platform that generates personalized quizzes on topics I choose, so that I can test my knowledge and improve my understanding through adaptive difficulty progression.

Acceptance Criteria

Understanding what "done" looks like helps us stay focused on delivering value:

1. Topic Selection: Users can input any academic topic and receive a relevant quiz within 30 seconds

2. Progressive Difficulty: The platform adjusts question difficulty based on user performance, maintaining appropriate challenge levels

3. Comprehensive Feedback: Users receive detailed explanations for both correct and incorrect answers

4. Performance Tracking: Users can view their improvement over time across different subjects

5. Accessibility: The platform works on mobile devices and follows web accessibility standards

Technical Requirements and Constraints

These requirements guide our technical decisions:

Functional Requirements:

• Support for multiple question types (multiple choice, true/false, fill-in-blank)

• Real-time quiz generation using Claude AI API

• User authentication and profile management

• Performance analytics and progress tracking

• Responsive web interface

Non-Functional Requirements:

• Quiz generation response time under 10 seconds for 10 questions

• Support for 100 concurrent users initially

• 99% uptime availability

• Mobile-responsive design

• GDPR-compliant data handling

Definition of "Done" for This Component

Today's component is complete when:

• Development environment is fully configured and tested

• Project repository is created with proper structure

• Requirements document is detailed and reviewed

• Basic project skeleton runs without errors

• All team members can clone and run the project locally

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3. SYSTEM DESIGN (45 minutes)

Architecture Diagram Instructions

Create a high-level architecture diagram showing these components and their relationships:

Component Layout:

[Web Browser] <---> [Frontend React App]
      ^                    |
      |                    v
[Mobile App] <---> [API Gateway/Load Balancer]
                          |
                          v
        [Backend Services Layer]
        ├── Authentication Service
        ├── Quiz Generation Service
        ├── User Management Service
        └── Analytics Service
                          |
                          v
        [External Services]
        ├── Claude AI API
        └── Database (MongoDB)

Drawing Instructions:

1. Use draw.io or any diagram tool

2. Show data flow with arrows

3. Label each connection with the type of communication (REST API, WebSocket, etc.)

4. Save as both editable format and PNG

Data Models and Schema Requirements

The core entities our system will manage:

User Entity:

• Unique identifier, email, password hash

• Profile information (name, educational level)

• Preferences and settings

• Creation and last login timestamps

Quiz Entity:

• Unique identifier, title, description

• Topic and subtopic classifications

• Difficulty level and question count

• Generated timestamp and creator reference

Question Entity:

• Question text, type, and difficulty level

• Answer options and correct answer indicators

• Explanation text for educational value

• Associated topic tags

Quiz Attempt Entity:

• User and quiz references

• Start time, completion time, and final score

• Individual question responses and accuracy

• Performance metrics for analytics

API Endpoints Specification

Define the RESTful API structure we'll build:

Authentication Endpoints:

POST /api/auth/register - User registration
POST /api/auth/login - User authentication  
POST /api/auth/logout - Session termination
GET /api/auth/profile - User profile retrieval

Quiz Management Endpoints:

POST /api/quiz/generate - Create new quiz from topic
GET /api/quiz/:id - Retrieve specific quiz
POST /api/quiz/:id/attempt - Start quiz attempt
PUT /api/quiz/attempt/:id - Submit answers
GET /api/quiz/attempts/user/:userId - User's quiz history

Business Logic Specifications

The core business rules that drive our application:

Quiz Generation Logic:

• Validate topic input and sanitize for AI consumption

• Send structured prompt to Claude API with difficulty parameters

• Parse and validate AI response for completeness and accuracy

• Store generated questions with proper metadata tagging

Progressive Difficulty Logic:

• Analyze user's last 5 quiz attempts in the topic area

• Calculate average accuracy and response time patterns

• Adjust starting difficulty level based on performance trends

• Implement fallback logic for new users without history

Integration Points with Other System Components

Understanding how components communicate helps prevent issues:

Claude AI Integration:

• RESTful API calls with authentication headers

• Structured prompt engineering for consistent question generation

• Response parsing and error handling for service reliability

• Rate limiting and cost management for API usage

Database Integration:

• MongoDB connection with connection pooling

• Data validation and sanitization before storage

• Indexing strategy for performance optimization

• Backup and recovery procedures for data protection

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4. STEP-BY-STEP IMPLEMENTATION (90 minutes)

Let's build the foundation of your AI Quiz Platform with careful attention to professional development practices.

Source Repository : https://github.com/sysdr/ai-quiz-platform

Project Structure Creation

Step 1: Create the main project directory

mkdir ai-quiz-platform
cd ai-quiz-platform

Step 2: Initialize Git repository

git init
git config user.name "Your Name"
git config user.email "your.email@example.com"

Step 3: Create comprehensive folder structure

# Backend structure
mkdir -p backend/src/{controllers,services,models,middleware,routes,utils,config}
mkdir -p backend/tests/{unit,integration,e2e}
mkdir -p backend/docs

# Frontend structure  
mkdir -p frontend/src/{components,pages,services,utils,styles,assets}
mkdir -p frontend/public

# DevOps and documentation
mkdir -p docker
mkdir -p docs/{requirements,architecture,api}
mkdir -p scripts

Your project tree should look like this:

ai-quiz-platform/
├── backend/
│   ├── src/
│   │   ├── controllers/
│   │   ├── services/
│   │   ├── models/
│   │   ├── middleware/
│   │   ├── routes/
│   │   ├── utils/
│   │   └── config/
│   ├── tests/
│   │   ├── unit/
│   │   ├── integration/
│   │   └── e2e/
│   └── docs/
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── services/
│   │   ├── utils/
│   │   ├── styles/
│   │   └── assets/
│   └── public/
├── docker/
├── docs/
│   ├── requirements/
│   ├── architecture/
│   └── api/
└── scripts/

Backend Foundation Setup

Step 4: Initialize backend Node.js project

cd backend
npm init -y

Step 5: Install essential dependencies

# Production dependencies
npm install express mongoose dotenv cors helmet express-rate-limit
npm install bcryptjs jsonwebtoken validator
npm install @anthropic-ai/sdk

# Development dependencies  
npm install -D nodemon jest supertest eslint prettier
npm install -D @types/node typescript ts-node

Step 6: Create backend configuration file

Create backend/src/config/database.js:

Step 7: Create Claude AI service configuration

Create backend/src/config/claude.js:

Step 8: Create main application entry point

Create backend/src/app.js:

Step 9: Create server entry point

Create backend/src/server.js:

Step 10: Create environment configuration

Create backend/.env.example:

Step 11: Create package.json scripts

Update backend/package.json scripts section:

{
  "scripts": {
    "start": "node src/server.js",
    "dev": "nodemon src/server.js",
    "test": "jest",
    "test:watch": "jest --watch",
    "test:coverage": "jest --coverage",
    "lint": "eslint src/**/*.js",
    "lint:fix": "eslint src/**/*.js --fix",
    "format": "prettier --write src/**/*.js"
  }
}

Project Documentation Setup

Step 12: Create comprehensive requirements document

Create docs/requirements/functional-requirements.md:

Step 13: Create initial Git repository setup

Create .gitignore:

Step 14: Initial commit and repository setup

# Add all files to git
git add .

# Create initial commit
git commit -m "Initial project setup with backend foundation

- Added Express.js server with security middleware
- Configured Claude AI integration
- Set up MongoDB connection handling
- Implemented proper error handling and logging
- Created comprehensive project structure
- Added development scripts and dependencies"

# Create and push to GitHub (replace with your repository URL)
git branch -M main
git remote add origin https://github.com/yourusername/ai-quiz-platform.git
git push -u origin main

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5. UNIT TESTING (45 minutes)

Professional software development requires comprehensive testing to ensure reliability and maintainability. Let's establish a robust testing foundation.

Test Cases to Implement

Test Case 1: Database Connection Validation Create backend/tests/unit/config/database.test.js:

Test Case 2: Claude AI Configuration Validation Create backend/tests/unit/config/claude.test.js:

Test Case 3: Application Initialization Create backend/tests/unit/app.test.js:

Test Execution Instructions

# Navigate to backend directory
cd backend

# Run all tests
npm test

# Run tests with coverage report
npm run test:coverage

# Run tests in watch mode for development
npm run test:watch

Coverage Requirements

Your tests should achieve:

• Line Coverage: Minimum 80%

• Function Coverage: Minimum 85%

• Branch Coverage: Minimum 75%

• Statement Coverage: Minimum 80%

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6. INTEGRATION TESTING (45 minutes)

Integration testing verifies that different components work together correctly. This level of testing catches issues that unit tests might miss.

API Integration Test Setup

Create backend/tests/integration/api.test.js:

Database Integration Tests

Create backend/tests/integration/database.test.js:

Running Integration Tests

# Make sure MongoDB is running locally
# Install and start MongoDB if not already running

# Run integration tests
npm run test -- --testPathPattern=integration

# Run integration tests with coverage
npm run test:coverage -- --testPathPattern=integration

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7. AUTOMATION SETUP (30 minutes)

Automation ensures consistent code quality and deployment processes. Let's set up continuous integration and deployment pipelines.

GitHub Actions CI/CD Setup

Create .github/workflows/ci.yml:

Pre-commit Hooks Setup

Create .husky/pre-commit:

Development Scripts

Create scripts/dev-setup.sh:

Make the script executable:

chmod +x scripts/dev-setup.sh

Quality Assurance Scripts

Create scripts/quality-check.sh:

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8. BUILD AND VERIFICATION (WITHOUT DOCKER) (30 minutes)

Let's verify that your application runs correctly in a native environment before containerizing it.

Step-by-Step Build Instructions

Step 1: Verify MongoDB is running

# Check if MongoDB is running
mongosh --eval "db.adminCommand('ismaster')"

# If not running, start it
# macOS with Homebrew:
brew services start mongodb/brew/mongodb-community

# Linux with systemd:
sudo systemctl start mongod

# Windows (run as administrator):
net start MongoDB

Step 2: Install and verify dependencies

# Navigate to backend directory
cd backend

# Install dependencies
npm install

# Verify installation
npm list --depth=0

Step 3: Environment configuration

# Copy environment template
cp .env.example .env

# Edit environment file (use your preferred editor)
nano .env

# Add your Claude API key:
# CLAUDE_API_KEY=your_actual_api_key_here

Step 4: Run development server

# Start in development mode
npm run dev

# The server should start on port 3000
# You should see output like:
# 📊 Database connected successfully
# 🤖 Claude AI API configured successfully
# 🚀 Server running on port 3000

Manual Verification Checklist

✅ Health Check Verification

# Test health endpoint
curl http://localhost:3000/health

# Expected response:
# {
#   "status": "healthy",
#   "timestamp": "2024-01-01T10:00:00.000Z",
#   "version": "1.0.0"
# }

✅ API Information Verification

# Test API info endpoint
curl http://localhost:3000/api

# Expected response:
# {
#   "message": "AI Quiz Platform API",
#   "version": "1.0.0",
#   "endpoints": {
#     "health": "/health",
#     "auth": "/api/auth",
#     "quiz": "/api/quiz"
#   }
# }

✅ Error Handling Verification

# Test 404 handling
curl http://localhost:3000/nonexistent

# Expected response:
# {
#   "error": "Route not found",
#   "path": "/nonexistent",
#   "method": "GET"
# }

✅ Database Connection Verification

# Check MongoDB connection in a separate terminal
mongosh

# In MongoDB shell:
use ai-quiz-platform
show collections

# Should show the database exists (may be empty initially)

Troubleshooting Common Issues

Issue: "Cannot connect to MongoDB"

# Solution 1: Check if MongoDB is running
ps aux | grep mongod

# Solution 2: Check MongoDB logs
tail -f /usr/local/var/log/mongodb/mongo.log  # macOS
tail -f /var/log/mongodb/mongod.log           # Linux

# Solution 3: Restart MongoDB
brew services restart mongodb/brew/mongodb-community  # macOS
sudo systemctl restart mongod                         # Linux

Issue: "Claude API key not working"

# Verify your API key in .env file
cat backend/.env | grep CLAUDE_API_KEY

# Test API key manually (replace with your key)
curl -X POST https://api.anthropic.com/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: your-api-key" \
  -d '{"model": "claude-3-sonnet-20240229", "max_tokens": 10, "messages": [{"role": "user", "content": "Hi"}]}'

Issue: "Port already in use"

# Check what's using port 3000
lsof -i :3000

# Kill the process if needed
kill -9 <PID>

# Or use a different port
PORT=3001 npm run dev

Performance Considerations

Memory Usage Monitoring

# Monitor Node.js memory usage
node --max-old-space-size=4096 src/server.js

# Or use built-in monitoring
npm install -g clinic
clinic doctor -- node src/server.js

Connection Pool Optimization Verify MongoDB connection pool settings in your database config are appropriate for your system:

• maxPoolSize: 10 for development

• Monitor active connections with MongoDB compass or shell

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9. CONTAINERIZATION (30 minutes)

Containerization ensures consistent deployment across different environments and simplifies dependency management.

Dockerfile Creation

Create backend/Dockerfile:

Docker Compose Configuration

Create docker-compose.yml in the project root:

Environment Configuration for Docker

Create docker/.env.example:

MongoDB Initialization Script

Create docker/mongodb/init-scripts/init-db.js:

Container Configuration Requirements

Create docker/nginx/nginx.conf (for future load balancing):

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10. BUILD AND VERIFICATION (WITH DOCKER) (30 minutes)

Now let's build and test your application using Docker containers to ensure it works in a containerized environment.

Docker Build Instructions

Step 1: Prepare environment configuration

# Create Docker environment file
cp docker/.env.example .env

# Edit the environment file with your actual values
nano .env

# Ensure you have your Claude API key set:
# CLAUDE_API_KEY=your_actual_claude_api_key

Step 2: Build Docker images

# Build backend image
docker build -t ai-quiz-backend ./backend

# Verify the image was created
docker images | grep ai-quiz-backend

# Expected output should show your image with latest tag

Step 3: Start services with Docker Compose

# Start all services in detached mode
docker-compose up -d

# Check that all services are running
docker-compose ps

# Expected output:
# NAME                COMMAND                  STATUS              PORTS
# ai-quiz-backend     "node src/server.js"     Up                  0.0.0.0:3000->3000/tcp
# ai-quiz-mongodb     "docker-entrypoint.s…"   Up (healthy)        0.0.0.0:27017->27017/tcp
# ai-quiz-redis       "redis-server --appe…"   Up (healthy)        0.0.0.0:6379->6379/tcp

Docker Run Commands for Individual Testing

Test backend container independently:

# Run MongoDB first
docker run -d --name test-mongodb \
  -e MONGO_INITDB_ROOT_USERNAME=admin \
  -e MONGO_INITDB_ROOT_PASSWORD=password123 \
  -p 27017:27017 \
  mongo:6.0

# Wait for MongoDB to be ready
sleep 10

# Run backend container
docker run -d --name test-backend \
  -e NODE_ENV=production \
  -e MONGODB_URI=mongodb://admin:password123@host.docker.internal:27017/ai-quiz-platform?authSource=admin \
  -e CLAUDE_API_KEY=your_api_key_here \
  -e JWT_SECRET=test-secret \
  -p 3001:3000 \
  ai-quiz-backend

# Test the containerized backend
curl http://localhost:3001/health

# Clean up test containers
docker stop test-backend test-mongodb
docker rm test-backend test-mongodb

Container-Based Testing Approach

Health Check Verification:

# Check health of all services
docker-compose exec backend curl http://localhost:3000/health

# Check MongoDB health
docker-compose exec mongodb mongo --eval "db.adminCommand('ismaster')"

# Check Redis health
docker-compose exec redis redis-cli ping

Log Inspection:

# View backend logs
docker-compose logs backend

# View MongoDB logs
docker-compose logs mongodb

# Follow logs in real-time
docker-compose logs -f backend

# View logs for specific time period
docker-compose logs --since "1h" backend

Performance Testing in Containers:

# Install testing tools in a temporary container
docker run --rm --network host alpine/curl:latest \
  curl -o /tmp/test-results.json \
  -w "@/dev/stdout" \
  -H "Content-Type: application/json" \
  -s http://localhost:3000/health

# Load testing with multiple requests
for i in {1..100}; do
  docker run --rm --network host alpine/curl:latest \
    curl -s http://localhost:3000/health > /dev/null && echo "Request $i: OK" || echo "Request $i: FAILED"
done

Container-Specific Troubleshooting

Issue: Container won't start

# Check container logs for errors
docker-compose logs backend

# Check if environment variables are set correctly
docker-compose exec backend env | grep -E "(CLAUDE|MONGODB|NODE_ENV)"

# Verify network connectivity
docker-compose exec backend ping mongodb

Issue: Database connection fails

# Check if MongoDB is accessible from backend
docker-compose exec backend mongosh mongodb://admin:password123@mongodb:27017/ai-quiz-platform?authSource=admin

# Verify MongoDB is accepting connections
docker-compose exec mongodb mongo --eval "db.adminCommand('ismaster')"

# Check network connectivity
docker network ls
docker network inspect ai-quiz-platform_quiz-platform-network

Issue: API calls fail

# Test API from inside container
docker-compose exec backend curl http://localhost:3000/api

# Test from host machine
curl http://localhost:3000/api

# Check port binding
docker port ai-quiz-backend

Resource Monitoring:

# Monitor container resource usage
docker stats

# Check container disk usage
docker system df

# View detailed container information
docker-compose exec backend ps aux
docker-compose exec backend df -h

Container Cleanup:

# Stop all services
docker-compose down

# Remove volumes (this will delete all data)
docker-compose down -v

# Remove unused images and containers
docker system prune -a

# Reset everything for fresh start
docker-compose down -v --remove-orphans
docker system prune -af

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11. KNOWLEDGE CHECK (15 minutes)

Test your understanding of today's concepts with these focused questions.

Question 1: Software Development Lifecycle

What are the key phases of the SDLC that we followed today, and why is each phase important for project success?

Answer: Today we followed these SDLC phases:

1. Requirements Analysis - Essential for understanding what we're building and defining success criteria

2. System Design - Creates the blueprint for implementation, preventing architectural issues

3. Implementation - The actual coding phase where we build the system components

4. Testing - Ensures code quality and functionality through unit and integration tests

5. Deployment - Makes the application available and verifiable in different environments

Each phase builds upon the previous one and helps catch issues early when they're cheaper to fix.

Question 2: Architecture Decisions

Explain why we chose a microservices architecture over a monolithic approach for this AI quiz platform, and what are the trade-offs?

Answer: Microservices Benefits:

• Scalability: Each service (auth, quiz generation, analytics) can scale independently

• Technology Flexibility: Could use different languages/frameworks for specialized services

• Fault Isolation: If one service fails, others continue working

• Team Organization: Different teams can work on different services

Trade-offs:

• Complexity: More complex deployment and service coordination

• Network Overhead: Inter-service communication has latency

• Data Consistency: Harder to maintain consistency across services

• Development Overhead: More boilerplate code for service communication

For our platform, microservices make sense because AI generation, user management, and analytics have different scaling and performance requirements.

Question 3: Testing Strategy

Why did we implement both unit tests and integration tests, and how do they complement each other?

Answer: Unit Tests:

• Test individual functions/components in isolation

• Fast execution and immediate feedback

• Catch logic errors and edge cases

• Easy to maintain and debug

Integration Tests:

• Test how components work together

• Verify API endpoints work end-to-end

• Catch configuration and environment issues

• Test actual database and external service interactions

They complement each other by providing different levels of confidence - unit tests ensure individual components work correctly, while integration tests ensure the complete system functions as expected.

Question 4: Environment Management

What's the difference between our development environment setup and Docker containerization, and when would you use each approach?

Answer: Development Environment (Native):

• Use for: Active development, debugging, quick iterations

• Benefits: Direct access to tools, faster startup, easier debugging

• Drawbacks: Environment inconsistency, dependency conflicts

Docker Containerization:

• Use for: Production deployment, testing, team collaboration

• Benefits: Environment consistency, easy deployment, isolation

• Drawbacks: Slower startup, debugging complexity, resource overhead

You'd use native development for daily coding and Docker for deployment and ensuring consistent environments across team members.

Question 5: Challenge Question - API Design

Design a RESTful API endpoint for retrieving a user's quiz history with filtering and pagination. Include the HTTP method, URL structure, query parameters, and response format.

Answer:

GET /api/users/{userId}/quiz-attempts?topic={topic}&difficulty={level}&page={page}&limit={limit}&sort={field}&order={asc|desc}

Query Parameters:
- topic (optional): Filter by quiz topic
- difficulty (optional): Filter by difficulty level (1-5)
- page (optional): Page number (default: 1)
- limit (optional): Items per page (default: 10, max: 100)
- sort (optional): Sort field (date, score, duration)
- order (optional): Sort order (asc, desc, default: desc)

Response Format:
{
  "data": [
    {
      "attemptId": "uuid",
      "quizId": "uuid",
      "topic": "JavaScript",
      "score": 85,
      "maxScore": 100,
      "completedAt": "2024-01-01T10:00:00Z",
      "duration": 300,
      "difficultyLevel": 3
    }
  ],
  "pagination": {
    "currentPage": 1,
    "totalPages": 5,
    "totalItems": 47,
    "itemsPerPage": 10,
    "hasNext": true,
    "hasPrevious": false
  },
  "filters": {
    "topic": "JavaScript",
    "difficulty": 3
  }
}

This design follows REST conventions, provides flexible filtering, and includes comprehensive metadata for client applications.

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12. HOMEWORK AND PREPARATION FOR NEXT DAY (15 minutes)

Consolidate today's learning and prepare for tomorrow's advanced topics.

Additional Exercises to Reinforce Learning

Exercise 1: Enhance Error Handling Add more sophisticated error handling to your application:

• Create custom error classes for different error types

• Implement error logging with different severity levels

• Add error reporting to an external service (simulate with console logs)

Exercise 2: Add Basic Validation Implement input validation for your API endpoints:

• Add request body validation for future POST endpoints

• Create middleware for parameter validation

• Implement sanitization for user inputs

Exercise 3: Expand Testing Coverage Write additional tests to improve coverage:

• Add edge case tests for error scenarios

• Test environment variable handling

• Create mock tests for external service dependencies

Resources for Deeper Understanding

Technical Documentation:

1. Express.js Guide: https://expressjs.com/en/guide/routing.html

2. MongoDB Node.js Driver: https://docs.mongodb.com/drivers/node/current/

3. Jest Testing Framework: https://jestjs.io/docs/getting-started

4. Docker Documentation: https://docs.docker.com/get-started/

Best Practices Reading:

1. Node.js Best Practices: https://github.com/goldbergyoni/nodebestpractices

2. API Design Guidelines: https://docs.microsoft.com/en-us/azure/architecture/best-practices/api-design

3. Testing Best Practices: https://kentcdodds.com/blog/common-mistakes-with-react-testing-library

AI Integration Resources:

1. Anthropic Claude API Documentation: https://docs.anthropic.com/claude/reference/getting-started-with-the-api

2. Prompt Engineering Guide:

https://www.promptingguide.ai/

1. AI Safety in Applications: https://docs.anthropic.com/claude/docs/use-case-guidelines

Preview of Next Day's Topic and Preparation Needed

Tomorrow: System Architecture Design and Database Schema Implementation

What we'll cover:

• Detailed system architecture design with component diagrams

• MongoDB schema design for users, quizzes, and questions

• Data relationships and indexing strategies

• API endpoint planning and documentation

Preparation needed:

1. Review MongoDB Basics: Refresh your knowledge of MongoDB operations, indexing, and schema design

2. Install MongoDB Compass: Download MongoDB Compass for visual database management

3. Study Data Modeling: Read about document database design patterns

4. Architecture Patterns: Research microservices communication patterns

Pre-work Assignment: Create a simple data model diagram for these entities:

• User (with authentication and profile data)

• Quiz (with metadata and configuration)

• Question (with multiple formats and difficulty)

• QuizAttempt (with performance tracking)

Think about the relationships between these entities and what queries you might need to support.

Environment Verification: Ensure your development environment is fully working:

# Test your setup
cd backend
npm run dev

# In another terminal, test endpoints
curl http://localhost:3000/health
curl http://localhost:3000/api

# Test database connection
mongosh
use ai-quiz-platform
db.runCommand({ ping: 1 })

Reflection Questions: Consider these questions as you review today's work:

1. What was the most challenging part of today's setup?

2. How would you explain the SDLC phases to someone new to software development?

3. What additional features would you want to add to the basic platform we started?

4. How would you handle scaling this application to support thousands of users?

Take time to commit your code, update your learning journal, and prepare for tomorrow's deeper dive into system design and database architecture!

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Congratulations! You've completed Day 1 of your AI Quiz Platform journey. You now have a solid foundation with proper project structure, development environment, testing framework, and deployment setup. Tomorrow we'll build upon this foundation to create a robust system architecture and implement the core data models.